PREDICTION MODEL FOR WHEEL LOADING IN GRINDING USING VIBRATION ANALYSIS AND ANN

K. Viswanathan,∗ A. Krishnakumari,∗∗ and D. Dinakaran∗∗∗

References

  1. [1] E. Susic and I. Grabec, Characterization of the grinding process by acoustic emission, International Journal of Machine Tools & Manufacture, 40, 2000, 225–238.
  2. [2] L. De Chiffre, P. Lonardo, and H. Trumpold, Quantitative characterization of surface texture, CIRP Annals, 49(2), 2000, 635–642,644–652.
  3. [3] J.S. Kwak and J.S. Bok, Trouble diagnosis of the grinding process by using acoustic emission signals, International Journal of Machine Tools & Manufacture, 41, 2001, 899–913.
  4. [4] A. Hassui and A.E. Diniz, Correlating surface roughness and vibration on plunge grinding of steel, International Journal of Machine Tools & Manufacture, 43, 2003, 855–862.
  5. [5] Q. Liu, X. Chen, and N. Gindy, Fuzzy pattern recognition of AE signals for grinding burn, International Journal of Machine Tools & Manufacture, 45, 2005, 811–818.
  6. [6] W.T. Liao, C.F. Ting, J. Qu, and P.J. Blau, Wavelet-based methodology for grinding wheel condition monitoring, International Journal of Machine Tools & Manufacture, 47, 2007, 580–592.
  7. [7] S. Malkin and C. Guo, Thermal analysis of grinding, CIRP Annals, 56(2), 2007, 760–782.
  8. [8] X. Huang and Y. Gao, A discrete system model for form error control in surface grinding, International Journal of Machine Tools & Manufacture, 50, 2010, 219–230.
  9. [9] R. Teti and K. Jemielniak, Advanced monitoring of machining operations, CIRP Annals, 59, 2010, 717–739.
  10. [10] V. Gopan and L.D. Wins, Quantitative analysis of grinding wheel loading using image processing, Procedia Technology, 25, 2016, 885–891.
  11. [11] J.-Y. Yang, B.-H. Xia, Z. Chen, T.-L. Li, and R. Liu, Vibrationbased structural damage identification: A review, International Journal of Robotics and Automation, 35(2), 2020. DOI: 10.2316/J.2020.206-0259.
  12. [12] I. Tanyer, E. Tatlicioglu, and E. Zergeroglu, Neural network based robust control of an aircraft, International Journal of Robotics and Automation, 35(1), 2020, DOI: 10.2316/J.2020.206-0074.
  13. [13] P. Kanakarajan, S. Sundaram, A. Kumaravel, R. Rajasekar, and R. Venkatachalam, Prediction of the surface roughness and wheel wear of modern ceramic material (Al2O3) during grinding using multiple regression analysis model, Indian Journal of Engineering and Materials Sciences, 24, 2017, 182–186.
  14. [14] G. Kant and K.S. Sangwan, Predictive modelling and optimization of machining parameters to minimize surface roughness using artificial neural network, Procedia CIRP, 31, 2015, 453–458.
  15. [15] E. Garc´ıa-Plaza, P.J. Núñez, D.R. Salgado, I. Cambero, J.M. Herrera Olivenza, and J. Garc´ıa Sanz-Calcedo, Surface finish monitoring in taper turning CNC using artificial neural network and multiple regression methods, Procedia Engineering, 63, 2013, 599–607.
  16. [16] C. Sreeprdha and A. Krishnakumari, Neural network model for condition monitoring of wear & film thickness in gear box, Neural Computer & Applications, 24, 2014, 1943–1952. 65

Important Links:

Go Back